Zahid Hamza, Chong Kil To, Tayara Hilal
Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Jeollabuk-do, Republic of Korea.
Advances Electronics and Information Research Centre, Jeonbuk National University, Jeonju 54896, Jeollabuk-do, Republic of Korea.
Molecules. 2025 Jul 6;30(13):2871. doi: 10.3390/molecules30132871.
Kinase is an enzyme responsible for cell signaling and other complex processes. Mutations or changes in kinase can cause cancer and other diseases in humans, including leukemia, neuroblastomas, glioblastomas, and more. Considering these concerns, inhibiting overexpressed or dysregulated kinases through small drug molecules is very important. In the past, many machine learning and deep learning approaches have been used to inhibit unregulated kinase enzymes. In this work, we employ a Graph Neural Network (GNN) to predict the inhibition activities of kinases. A separate Graph Convolution Network (GCN) and combined Graph Convolution and Graph Attention Network (GCN_GAT) are developed and trained on two large datasets (Kinase Datasets 1 and 2) consisting of small drug molecules against the targeted kinase using 10-fold cross-validation. Furthermore, a wide range of molecules are used as independent datasets on which the performance of the models is evaluated. On both independent kinase datasets, our model combining GCN and GAT provides the best evaluation and outperforms previous models in terms of accuracy, Matthews Correlation Coefficient (MCC), sensitivity, specificity, and precision. On the independent Kinase Dataset 1, the values of accuracy, MCC, sensitivity, specificity, and precision are 0.96, 0.89, 0.90, 0.98, and 0.91, respectively. Similarly, the performance of our model combining GCN and GAT on the independent Kinase Dataset 2 is 0.97, 0.90, 0.91, 0.99, and 0.92 in terms of accuracy, MCC, sensitivity, specificity, and precision, respectively.
激酶是一种负责细胞信号传导和其他复杂过程的酶。激酶的突变或变化会导致人类患上癌症和其他疾病，包括白血病、神经母细胞瘤、胶质母细胞瘤等等。考虑到这些问题，通过小分子药物抑制过度表达或失调的激酶非常重要。过去，许多机器学习和深度学习方法已被用于抑制不受调控的激酶酶。在这项工作中，我们采用图神经网络（GNN）来预测激酶的抑制活性。我们开发了一个单独的图卷积网络（GCN）以及结合图卷积和图注意力网络（GCN_GAT），并在两个大型数据集（激酶数据集1和2）上进行训练，这两个数据集由针对靶向激酶的小分子药物组成，采用10折交叉验证。此外，我们使用了广泛的分子作为独立数据集来评估模型的性能。在两个独立的激酶数据集上，我们结合GCN和GAT的模型提供了最佳评估，并且在准确性、马修斯相关系数（MCC）、敏感性、特异性和精确性方面均优于先前的模型。在独立的激酶数据集1上，准确性、MCC、敏感性、特异性和精确性的值分别为0.96、0.89、0.90、0.98和0.91。同样，我们结合GCN和GAT的模型在独立的激酶数据集2上的性能，在准确性、MCC、敏感性、特异性和精确性方面分别为0.97、0.90、0.91、0.99和0.92。
